Voxel-based radiomics outlines spatial heterogeneity of cerebral radiation necrosis (RN) associated with bevacizumab (Bev) response in head and neck radiotherapy (RT) patients.
Authors: Hong Qi Tan, Jinhua Cai, Adelene Y.L. Sim, Luo Huang, Melvin L.K. Lee Kiang Chua, Yamei Tang
Journal: Journal of Clinical Oncology (2022)

e18063 Background: RN is a debilitating toxicity of head and neck RT, with incidences of 4% to 24%. Bev is a promising drug for managing RN, but there is currently no biomarker that could predict a priori if a patient would benefit from Bev. Here, we aimed to use radiomics to predict Bev response. However, current radiomics tools suffer from issues of interrater contouring variability and explanability of the radiomics models. We therefore investigated a voxel-based radiomics approach to characterize the spatial heterogeneity of RN, and define subregions that are associated with Bev response. Methods: 118 consecutive nasopharyngeal carcinoma (NPC) patients who were diagnosed with RN post-RT, and were treated with Bev between Jul 2012 and Mar 2019 at the Sun Yat-Sen Memorial Hospital were enrolled into the study. 77 patients with 101 brain lesions treated between Jul 2012 and Dec 2017 were assigned to the training set, and 41 patients with 51 brain lesions treated between Jan 2018 and Mar 2019 were used for validation. All patients received Bev prescribed at 5 mg/kg every fortnightly for up to 4 courses. We extracted voxel-based radiomics features from each segment drawn on T2 FLAIR MRI images, followed by a 3-step analysis (individual- and population-level clustering, before delta-radiomics) to obtain a clustered heat map of subregions within the RN lesion. Anatomical correlation to areas of edema, necrosis, and their interphase was performed. Delta-radiomics tracked the ∆ of each cluster to Bev. Features were then extracted from each radiomics cluster for model building. Results: 71 (70.3%) and 34 (66.7%) lesions had documented radiological responses to Bev in the training and validation sets, respectively. Responders (N = 105) and non-responders (N = 47) showed a 71.8% (IQR: 54.7-87.5) and 13.2% (IQR: 7.98-21.4) reduction in RN volumes, respectively. Five optimal clusters were determined using the shoulder method with ΔK metric. Spatial analyses revealed that 2 of the 5 clusters were associated with the edema areas, with 92.6% and 74.3% of the voxels located within this anatomic subregion. The two corresponding clusters were significantly associated with a response to Bev (Odds ratio [OR]: 11.12 (95% CI: 2.54-73.47), P = 0.004; OR: 1.63 [1.07-2.78], P = 0.042). Finally, we developed and validated a clinicoradiomics model based on features extracted from the 2 clusters that improved the prediction of Bev response, compared with a clinical-only model for both the training (AUC 0.852 vs 0.755) and validation (AUC 0.816 vs 0.691) cohorts. Conclusions: Our spatial radiomics approach revealed anatomic subregions that contain relevant radiomics features that could predict Bev efficacy in the treatment of RN in NPC patients. This method would improve the interpretability of radiomics, compared with current methods that lack spatial resolution.